Jishnu Das, Ph.D.

  • Assistant Professor
  • Departments of Immunology and Computational & Systems Biology
  • Core Faculty Member, Center for Systems Immunology

Education & Training

  • BTech - Indian Institute of Technology, Kanpur (2006-2010)
  • PhD – Cornell University (2010-2016)
  • Ragon Institute of MGH, MIT & Harvard and MIT Bioengineering (2016-2019)

Research Interest Summary

We are a systems immunology lab that uses machine learning, high dimensional statistical and network biology approaches to uncover molecular phenotypes in immunological disorders.

Research Categories

Research Interests

We are a computational systems immunology lab. Our research focuses on the development and use of novel systems approaches to analyze high-dimensional immunological datasets, and elucidate molecular mechanisms of immunological disorders. Our past work has utilized systems approaches to analyze Mendelian mutations in the context of three-dimensional protein-protein interaction networks, to understand molecular mechanisms of corresponding disorders. We have also developed network analyses frameworks to characterize the evolutionary dynamics of these protein networks. Another key dimension of our past work has been the use of statistical and machine-learning approaches for the analyses of high-dimensional antibody-omic to elucidate correlates of vaccine-mediated and natural immunity in HIV, tuberculosis and malaria.

We are currently working on using network systems and functional genomic approaches to perform multi-scale integration of genomic and epigenomic datasets with biological networks to identify molecular phenotypes underlying these immunological disorders, with an emphasis on autoimmune and alloimmune diseases. We also use high-dimensional statistical and machine-learning techniques to integrate multi-omic datasets (genomic, transcriptomic, proteomic, metabolomic and antibody-omic) and elucidate molecular mechanisms of immune regulation and dysregulation.

Representative Publications

Das J et al, “Mining for humoral correlates of HIV control and latent reservoir size” PLoS Pathogens 2020 16(10): e1008868

Suscovich T*, Fallon J*, Das J* et al, “Mapping functional humoral correlates of protection against malaria challenge following RTS,S/AS01 vaccination” Science Translational Medicine 2020 12(553):eabb4757 *=co-first 

Bing X, Bunea F, Royer M, Das J^ “Latent model-based clustering for biological discovery”. iScience (Cell Press) 2019; 14:125-135 ^=corresponding author

Fragoza R, Das J* et al, “Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations.” Nature Communications 2019; 10(1):4141 *=co-first

Ackerman M, Das J et al, “Route of immunization defines multiple mechanisms of vaccine-mediated protection against SIV.” Nature Medicine 2018; 24(10): 1590-1598.

Vo T*, Das J* et al, “A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.” Cell 2016; 164(1-2):310-323 *=co-first

Wei X*, Das J* et al, “A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations.” PLoS Genetics 2014; 10(12): e1004819 *=co-first

Das J et al, “Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways.” Science Signaling 2013; 6(276):ra38.

Das J et al, “HINT: High-quality protein interactomes and their applications in understanding human disease.” BMC Systems Biology 2012; 6:92

Wang X*, Wei X*, Thijssen B, Das J* et al, “Three-dimensional reconstruction of protein networks provides insight into human genetic disease.” Nature Biotechnology 2012; 30(2): 159-164 *=co-first

Full List of Publications